Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer’s Disease

Carol J. Huseby, Elaine Delvaux, Danielle L. Brokaw, Paul D. Coleman

Research output: Contribution to journalArticlepeer-review

Abstract

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Original languageEnglish (US)
Article number1592
JournalBiomolecules
Volume12
Issue number11
DOIs
StatePublished - Nov 2022

Keywords

  • Alzheimer’s disease
  • amyotrophic lateral sclerosis or Lou Gehrig’s disease
  • Friedreich’s ataxia
  • frontotemporal dementia
  • Huntington’s disease
  • linear discriminant analysis
  • machine learning
  • Parkinson’s disease
  • random forest classification
  • whole blood RNA

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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